Institute of Biostatistics and Registry Research, Brandenburg Medical School Theodor Fontane, Fehrbelliner Straße 39, Neuruppin, 16816, Germany.
Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
BMC Med Res Methodol. 2023 Jun 29;23(1):154. doi: 10.1186/s12874-023-01982-w.
Many scientific papers are published each year and substantial resources are spent to develop biomarker-based tests for precision oncology. However, only a handful of tests is currently used in daily clinical practice, since development is challenging. In this situation, the application of adequate statistical methods is essential, but little is known about the scope of methods used.
A PubMed search identified clinical studies among women with breast cancer comparing at least two different treatment groups, one of which chemotherapy or endocrine treatment, by levels of at least one biomarker. Studies presenting original data published in 2019 in one of 15 selected journals were eligible for this review. Clinical and statistical characteristics were extracted by three reviewers and a selection of characteristics for each study was reported.
Of 164 studies identified by the query, 31 were eligible. Over 70 different biomarkers were evaluated. Twenty-two studies (71%) evaluated multiplicative interaction between treatment and biomarker. Twenty-eight studies (90%) evaluated either the treatment effect in biomarker subgroups or the biomarker effect in treatment subgroups. Eight studies (26%) reported results for one predictive biomarker analysis, while the majority performed multiple evaluations, either for several biomarkers, outcomes and/or subpopulations. Twenty-one studies (68%) claimed to have found significant differences in treatment effects by biomarker level. Fourteen studies (45%) mentioned that the study was not designed to evaluate treatment effect heterogeneity.
Most studies evaluated treatment heterogeneity via separate analyses of biomarker-specific treatment effects and/or multiplicative interaction analysis. There is a need for the application of more efficient statistical methods to evaluate treatment heterogeneity in clinical studies.
每年都有大量的科学论文发表，并且投入了大量资源来开发基于生物标志物的精准肿瘤学检测方法。然而，目前只有少数检测方法在日常临床实践中使用，因为开发具有挑战性。在这种情况下，应用适当的统计方法至关重要，但目前对于所使用的方法的范围了解甚少。
通过 PubMed 搜索，确定了比较至少两种不同治疗组的乳腺癌女性的临床研究，其中一组为化疗或内分泌治疗，通过至少一种生物标志物的水平。本综述纳入了在 15 种选定期刊之一中发表的 2019 年原始数据的研究。由三位评审员提取临床和统计特征，并报告了每一项研究的特征选择。
在查询中确定了 164 项研究，其中 31 项符合纳入标准。评估了超过 70 种不同的生物标志物。22 项研究（71%）评估了治疗与生物标志物之间的乘法交互作用。28 项研究（90%）评估了生物标志物亚组中的治疗效果或治疗亚组中的生物标志物效果。8 项研究（26%）报告了一个预测生物标志物分析的结果，而大多数研究进行了多次评估，包括几个生物标志物、结局和/或亚组。21 项研究（68%）声称已经发现了生物标志物水平与治疗效果之间的显著差异。14 项研究（45%）提到研究的设计并非为了评估治疗效果的异质性。
大多数研究通过单独分析生物标志物特异性治疗效果和/或乘法交互作用分析来评估治疗异质性。在临床研究中，需要应用更有效的统计方法来评估治疗异质性。
